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American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation
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In: Sensors ; Volume 21 ; Issue 17 (2021)
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| 2 |
Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®
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In: Multimodal Technologies and Interaction ; Volume 4 ; Issue 1 (2020)
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La potenciación descortés del desacuerdo en hablantes españoles e ingleses ; Impolite boosting of disagreement in Spanish and English speakers
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| 4 |
Interactional Metadiscourse In Doctoral Thesis Writing: A Study in Kenya
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In: Applied Linguistics Research Journal, Vol 4, Iss 4, Pp 100-113 (2020) (2020)
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| 5 |
Computing Happiness from Textual Data
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In: Stats ; Volume 2 ; Issue 3 ; Pages 25-370 (2019)
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| 6 |
Arabic-SOS: Segmentation, stemming, and orthography standardization for classical and pre-modern standard Arabic
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| 7 |
Computing Happiness from Textual Data
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In: 2 ; 3 ; 347 ; 370 (2019)
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| 9 |
IRISA at DeFT2017 : classification systems of increasing complexity ; Participation de l'IRISA à DeFT2017 : systèmes de classification de complexité croissante
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In: DeFT 2017 - Défi Fouille de texte ; https://hal.archives-ouvertes.fr/hal-01643993 ; DeFT 2017 - Défi Fouille de texte, Jun 2017, Orléans, France. pp.1-10 (2017)
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| 10 |
The Functions of Narrative Passages in Three Written Online Health Contexts
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In: Open Linguistics, Vol 2, Iss 1 (2016) (2016)
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| 11 |
ОБЗОР МЕТОДОВ И АЛГОРИТМОВ РАЗРЕШЕНИЯ ЛЕКСИЧЕСКОЙ МНОГОЗНАЧНОСТИ: ВВЕДЕНИЕ
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| 12 |
IRISA at DeFT 2015: Supervised and Unsupervised Methods in Sentiment Analysis
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In: DeFT, Défi Fouille de Texte, joint à la conférence TALN 2015 ; https://hal.archives-ouvertes.fr/hal-01226528 ; DeFT, Défi Fouille de Texte, joint à la conférence TALN 2015, Jun 2015, Caen, France (2015)
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| 13 |
A nonparametric Bayesian perspective for machine learning in partially-observed settings ...
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| 14 |
A nonparametric Bayesian perspective for machine learning in partially-observed settings
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Abstract:
Indiana University-Purdue University Indianapolis (IUPUI) ; Robustness and generalizability of supervised learning algorithms depend on the quality of the labeled data set in representing the real-life problem. In many real-world domains, however, we may not have full knowledge of the underlying data-generating mechanism, which may even have an evolving nature introducing new classes continually. This constitutes a partially-observed setting, where it would be impractical to obtain a labeled data set exhaustively defined by a fixed set of classes. Traditional supervised learning algorithms, assuming an exhaustive training library, would misclassify a future sample of an unobserved class with probability one, leading to an ill-defined classification problem. Our goal is to address situations where such assumption is violated by a non-exhaustive training library, which is a very realistic yet an overlooked issue in supervised learning. In this dissertation we pursue a new direction for supervised learning by defining self-adjusting models to relax the fixed model assumption imposed on classes and their distributions. We let the model adapt itself to the prospective data by dynamically adding new classes/components as data demand, which in turn gradually make the model more representative of the entire population. In this framework, we first employ suitably chosen nonparametric priors to model class distributions for observed as well as unobserved classes and then, utilize new inference methods to classify samples from observed classes and discover/model novel classes for those from unobserved classes. This thesis presents the initiating steps of an ongoing effort to address one of the most overlooked bottlenecks in supervised learning and indicates the potential for taking new perspectives in some of the most heavily studied areas of machine learning: novelty detection, online class discovery and semi-supervised learning.
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Keyword:
Bayesian statistical decision theory -- Research -- Analysis -- Evaluation; Boosting (Algorithms); Computational intelligence; Computational linguistics; Data mining; Discourse analysis -- Statistical methods; Machine learning; Mathematical statistics; Mathematical statistics -- Data processing; Nonexhaustive; Nonparametric Bayesian; Nonparametric statistics -- Research; Semi-supervised; Statistical decision; Statistics -- Data processing; Stochastic processes; Supervised; Supervised learning (Machine learning) -- Research
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URL: https://hdl.handle.net/1805/4825
https://doi.org/10.7912/C2/2316
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| 15 |
All cumulative semantic interference is not equal: A test of the Dark Side Model of lexical access
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| 16 |
Sign Language Recognition using Sub-Units
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In: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/2012/Cooper_JMLR_2012.pdf (2012)
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| 17 |
Boosting of fuzzy rules with low quality data
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In: http://sci2s.ugr.es/publications/ficheros/JMVLSC2011.pdf (2011)
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| 18 |
Adasum: an adaptive model for summarization
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In: http://www.cs.fiu.edu/%7Elli003/Sum/CIKM/2008/p901-zhang.pdf (2008)
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| 19 |
A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms. Discovery Science 2006
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In: http://www.inf.u-szeged.hu/~rfarkas/ds_lnai.pdf (2006)
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| 20 |
The ICSI+ Multilingual Sentence Segmentation System
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In: DTIC (2006)
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